About

The Question That Started This

We arrived at a ryokan in Kanazawa during a heavy snowstorm, dragging suitcases through six inches of fresh powder. The door opened before we knocked.

The host—no English, but perfect communication—welcomed us with a bow and gestures that said you're cold, come in, let me take those. We left to explore the town. When we returned ninety minutes later, our suitcases had been unpacked. Kimonos hung on the wall, ready for dinner. A handwritten note on the table with timing for the evening onsen.

I kept returning to the same question: How did they know?

Not the unpacking—that's procedure. But the sequence. The way she opened the door before we knocked. The way they let us explore before settling us in. The timing of the tea. The choreography that made everything feel effortless when it was meticulously designed.

That question—how do practitioners anticipate needs under constraint—is what this research explores.

What I'm Investigating

Omotenashi is often translated as "hospitality," but it's closer to anticipatory care—the discipline of seeing around corners, reading context, making others feel seen without making the care visible.

This journal documents that discipline through direct field observation.

I walk hotels, vacation rentals, cafés, wellness spaces. I time arrivals, sketch service flows, interview front desk staff, GMs, cleaners, owners. I ask what works, what breaks, what they wish existed. I document the patterns that make care human under real constraints—time pressure, language barriers, system failures, peak hours.

The secondary question: Can intelligence tools enhance that judgment rather than replace it?

Most hospitality AI is a black box. You automate, it responds, but operators have no idea why—or how to correct it when it fails. I'm exploring an alternative: transparent cognitive architectures that can explain their reasoning, show their work, and learn from human practitioners.

This sits at the intersection of anthropology, service design, and explainable AI. I'm treating hospitality as a laboratory for understanding how humans make judgment calls under uncertainty—then investigating whether technology can elevate rather than degrade that intelligence.

How I Got Here

I'm a product designer and engineer by training, originally from Venezuela, now based between Canada and travels across Europe and Asia.

My background taught me different ways of seeing systems:

• Engineering (McGill) trained me to observe carefully, measure precisely, ask what's broken and why. When I watch a check-in ritual, I'm sketching the service flow, timing the interactions, noting where friction occurs.
• Product design (Google, Sonder) taught me to center users—in this case, operators. The GM keeping a paper backup log when the PMS fails. The front desk agent who reads a tired guest's body language and adjusts their script. That frontline intelligence is what I'm studying.
• Strategy consulting (McKinsey) showed me how to see patterns across operations, industries, cultures. I learned to interview practitioners, extract tacit knowledge, distill principles that transfer. That's the method I'm applying to hospitality.
• Business training (Harvard MBA) helped me understand operator economics—the constraints that make "just hire more staff" or "build custom software" unrealistic for small teams. I'm designing for that reality, not ideal conditions.

But what stuck most wasn't the frameworks. It was watching practitioners make care workdespite their tools. The café owner in Barcelona who manually tracked regular customers' orders because the POS couldn't. The vacation rental manager who kept detailed notes on guest preferences that no platform captured. The hotel GM who could predict which guests would need early check-in just from reading the booking context.

That's the intelligence I'm documenting—and eventually, investigating how to operationalize without losing what makes it human.

Research Approach

This is field research with an ethnographic lens:

• Observation: I visit properties to watch care in practice. I time rituals, note what operators do under pressure, document failure modes. I pay attention to the unglamorous edges—the 19 arrivals in 14 minutes, the printer jam during check-in, the guest who arrives speaking a language no one on staff knows.
• Interviews: I talk to operators at every level. Not just managers—frontline staff who make the calls in real time. I ask about their best moments, their worst failures, the tools they wish existed, the rituals they've developed that aren't written down anywhere.
• Pattern extraction: I look for principles that transfer. What does a Barcelona hostel have in common with a Kanazawa ryokan? What separates competent service from exceptional anticipation? How do expert operators make judgment calls under uncertainty?
• Ethical boundaries: I anonymize by default, ask permission, compensate where appropriate, and give operators the right to withdraw insights. I never share identifiable guest data. I credit the practitioners whose intelligence I'm studying.
• Technical experiments: I explore whether transparent cognitive architectures—particularly BDI (Beliefs-Desires-Intentions) frameworks—can model how operators think. I test natural language reasoning, explainability traces, graceful failure modes. I share what works and what doesn't.

This year: Barcelona, Kanazawa, Lisbon, Paris, Berlin. Boutique hotels, vacation rentals, cafés, ryokans. Every layer, from cleaning protocols to owner strategy.

Open Questions I'm Pursuing

Cognitive modeling:

• How do expert concierges decide what's urgent vs. what can wait?
• What mental models do hosts use to read context from limited information?
• How do operators balance policy with personalization?

System design:

• What does "explainability" actually mean to a front desk agent troubleshooting a recommendation?
• How much context is enough for a system to feel attentive vs. invasive?
• What does graceful failure look like when technology breaks during a service moment?

Anticipatory intelligence:

• Can systems learn from operator corrections without requiring explicit training?
• How do you design technology that matches the warmth of human greeting?
• What artifacts (logs, scripts, checklists) do operators actually use vs. what software assumes they need?

Care at scale:

• How do small teams (3-5 people) deliver personalized care to dozens of guests simultaneously?
• What gets lost when you automate parts of a service ritual?
• Where is the line between "enhancing human judgment" and "replacing human judgment"?

I don't have answers yet. That's the point of the research.

Who I Hope to Hear From

Operators who want to share their rituals or nominate moments where care worked—or broke spectacularly.

If you run a boutique hotel, vacation rental, café, or small hospitality operation:

• Share your "first five minutes" protocol—what you do in the moments after a guest arrives
• Tell me about a system that failed and how you adapted
• Nominate your property for field observation (I offer service audits in exchange)

I'm particularly interested in small teams (under 20 staff) who deliver exceptional care without enterprise budgets.

Researchers and builders exploring transparent AI, explainable systems, cognitive architectures, or human-robot interaction.

If you're working on:

• BDI frameworks or other symbolic reasoning approaches
• Natural language systems that can explain their outputs
• Social robotics or embodied AI for service environments
• Tools that enhance human judgment rather than automate it away

Let's compare notes. I'm happy to share field observations, technical experiments, and patterns I'm seeing.

Anyone curious about what hospitality can teach us about designing intelligent systems that feel human.

If you've encountered technology that either enhanced or degraded a care moment—in hotels, healthcare, eldercare, anywhere humans anticipate needs—I want to hear about it. I'm collecting examples of what works and what breaks.

What I'm Not Doing (Yet)

I'm not pitching a product. I'm not fundraising. I'm not promising solutions.

Right now, I'm in research mode—documenting patterns, testing hypotheses, building relationships with operators who might eventually become design partners.

Eventually, I may explore whether the architectures I'm investigating could support real operations. But that's downstream. First, I need to understand what makes anticipatory care possible in the first place.

This journal is that exploration, documented in public.

How to Follow Along

• Subscribe for weekly field notes, operator interviews, and technical experiments.
• Reply to any post with your own observations—I learn as much from readers as from field research.
• Reach out if you want to collaborate, nominate a property for observation, or just talk about transparent intelligence and the future of care.

You can find me at carlos@omote.io or follow the research at omote.io/journal.

Why "Omotenashi"

The word comes from Japanese hospitality tradition, but the discipline is global. I've seen it in ryokans, Parisian cafés, Barcelona hostels, family-run pensions across Europe.

It's the intelligence of anticipation. The choreography that makes care feel effortless. The judgment that comes from deep context and hard-won experience.

That's what's worth studying. That's what's worth protecting as we build intelligent systems.